Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations7381
Missing cells195949
Missing cells (%)54.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory384.0 B

Variable types

Text8
Numeric14
DateTime6
Boolean5
Categorical16

Alerts

preventTargetGapPoints has constant value "True" Constant
userFlaggedNewItem has constant value "True" Constant
pointsNotAwardedReason has constant value "Action not allowed for user and CPG" Constant
deleted has constant value "True" Constant
competitiveProduct is highly overall correlated with competitorRewardsGroup and 16 other fieldsHigh correlation
competitorRewardsGroup is highly overall correlated with competitiveProduct and 8 other fieldsHigh correlation
discountedItemPrice is highly overall correlated with competitiveProduct and 9 other fieldsHigh correlation
finalPrice is highly overall correlated with competitiveProduct and 18 other fieldsHigh correlation
hours_diff is highly overall correlated with competitorRewardsGroup and 12 other fieldsHigh correlation
itemNumber is highly overall correlated with competitiveProduct and 18 other fieldsHigh correlation
itemPrice is highly overall correlated with discountedItemPrice and 14 other fieldsHigh correlation
items_in_receipt_list is highly overall correlated with hours_diff and 17 other fieldsHigh correlation
minutes_diff is highly overall correlated with competitorRewardsGroup and 12 other fieldsHigh correlation
needsFetchReview is highly overall correlated with competitorRewardsGroup and 16 other fieldsHigh correlation
needsFetchReviewReason is highly overall correlated with competitiveProduct and 17 other fieldsHigh correlation
originalFinalPrice is highly overall correlated with competitiveProduct and 21 other fieldsHigh correlation
originalMetaBriteBarcode is highly overall correlated with competitiveProduct and 21 other fieldsHigh correlation
originalMetaBriteDescription is highly overall correlated with finalPrice and 21 other fieldsHigh correlation
originalMetaBriteItemPrice is highly overall correlated with competitiveProduct and 21 other fieldsHigh correlation
originalMetaBriteQuantityPurchased is highly overall correlated with competitiveProduct and 21 other fieldsHigh correlation
partnerItemId is highly overall correlated with competitiveProduct and 14 other fieldsHigh correlation
pointsEarned is highly overall correlated with competitiveProduct and 18 other fieldsHigh correlation
pointsPayerId is highly overall correlated with finalPrice and 15 other fieldsHigh correlation
priceAfterCoupon is highly overall correlated with competitiveProduct and 5 other fieldsHigh correlation
purchasedItemCount is highly overall correlated with hours_diff and 17 other fieldsHigh correlation
quantityPurchased is highly overall correlated with itemNumber and 7 other fieldsHigh correlation
rewardsProductPartnerId is highly overall correlated with competitiveProduct and 16 other fieldsHigh correlation
summary_list_difference_count is highly overall correlated with competitorRewardsGroup and 12 other fieldsHigh correlation
targetPrice is highly overall correlated with competitiveProduct and 21 other fieldsHigh correlation
total_receipt_points_earned is highly overall correlated with hours_diff and 11 other fieldsHigh correlation
total_receipt_spent is highly overall correlated with hours_diff and 17 other fieldsHigh correlation
userFlaggedBarcode is highly overall correlated with competitiveProduct and 21 other fieldsHigh correlation
userFlaggedDescription is highly overall correlated with finalPrice and 17 other fieldsHigh correlation
userFlaggedPrice is highly overall correlated with competitiveProduct and 19 other fieldsHigh correlation
userFlaggedQuantity is highly overall correlated with competitiveProduct and 17 other fieldsHigh correlation
needsFetchReviewReason is highly imbalanced (77.4%) Imbalance
purchasedItemCount has 484 (6.6%) missing values Missing
finisheddate has 1411 (19.1%) missing values Missing
pointsawardeddate has 1301 (17.6%) missing values Missing
purchasedate has 458 (6.2%) missing values Missing
total_receipt_points_earned has 1128 (15.3%) missing values Missing
total_receipt_spent has 435 (5.9%) missing values Missing
barcode has 4291 (58.1%) missing values Missing
description has 821 (11.1%) missing values Missing
finalPrice has 614 (8.3%) missing values Missing
itemPrice has 614 (8.3%) missing values Missing
needsFetchReview has 6568 (89.0%) missing values Missing
partnerItemId has 440 (6.0%) missing values Missing
preventTargetGapPoints has 7023 (95.1%) missing values Missing
quantityPurchased has 614 (8.3%) missing values Missing
userFlaggedBarcode has 7044 (95.4%) missing values Missing
userFlaggedNewItem has 7058 (95.6%) missing values Missing
userFlaggedPrice has 7082 (95.9%) missing values Missing
userFlaggedQuantity has 7082 (95.9%) missing values Missing
needsFetchReviewReason has 7162 (97.0%) missing values Missing
pointsNotAwardedReason has 7041 (95.4%) missing values Missing
pointsPayerId has 6114 (82.8%) missing values Missing
rewardsGroup has 5650 (76.5%) missing values Missing
rewardsProductPartnerId has 5112 (69.3%) missing values Missing
userFlaggedDescription has 7176 (97.2%) missing values Missing
originalMetaBriteBarcode has 7310 (99.0%) missing values Missing
originalMetaBriteDescription has 7371 (99.9%) missing values Missing
brandCode has 4781 (64.8%) missing values Missing
competitorRewardsGroup has 7106 (96.3%) missing values Missing
discountedItemPrice has 1612 (21.8%) missing values Missing
originalReceiptItemText has 1621 (22.0%) missing values Missing
itemNumber has 7228 (97.9%) missing values Missing
originalMetaBriteQuantityPurchased has 7366 (99.8%) missing values Missing
pointsEarned has 6454 (87.4%) missing values Missing
targetPrice has 7003 (94.9%) missing values Missing
competitiveProduct has 6736 (91.3%) missing values Missing
originalFinalPrice has 7372 (99.9%) missing values Missing
originalMetaBriteItemPrice has 7372 (99.9%) missing values Missing
deleted has 7372 (99.9%) missing values Missing
priceAfterCoupon has 6425 (87.0%) missing values Missing
metabriteCampaignId has 6518 (88.3%) missing values Missing
items_in_receipt_list has 464 (6.3%) missing values Missing
summary_list_difference_count has 513 (7.0%) missing values Missing
hours_diff has 1301 (17.6%) missing values Missing
minutes_diff has 1301 (17.6%) missing values Missing
receipt_item_id has unique values Unique
partnerItemId has 145 (2.0%) zeros Zeros
summary_list_difference_count has 6756 (91.5%) zeros Zeros
hours_diff has 439 (5.9%) zeros Zeros
minutes_diff has 439 (5.9%) zeros Zeros

Reproduction

Analysis started2025-02-04 04:56:57.447466
Analysis finished2025-02-04 05:02:01.983846
Duration5 minutes and 4.54 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct1119
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:02.305752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters177144
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique817 ?
Unique (%)11.1%

Sample

1st row5ff1e1cd0a720f052300056f
2nd row5ff5d1f20a720f05230005db
3rd row5ff5d1f20a720f05230005db
4th row5ff5d1f20a720f05230005db
5th row5ff5d1f20a720f05230005db
ValueCountFrequency (%)
600f2fc80a720f0535000030 459
 
6.2%
600f39c30a7214ada2000030 450
 
6.1%
600f24970a720f053500002f 381
 
5.2%
600f0cc70a720f053500002c 217
 
2.9%
600a1a8d0a7214ada2000008 203
 
2.8%
60049d9d0a720f05f3000094 194
 
2.6%
60025cb80a720f05f300008d 185
 
2.5%
600260210a720f05f300008f 183
 
2.5%
600a1e270a720f0535000009 176
 
2.4%
600edb570a720f053500001d 155
 
2.1%
Other values (1109) 4778
64.7%
2025-02-04T05:02:02.927241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 62005
35.0%
a 15529
 
8.8%
2 14350
 
8.1%
f 11783
 
6.7%
7 10142
 
5.7%
5 9317
 
5.3%
6 8434
 
4.8%
3 8155
 
4.6%
1 6774
 
3.8%
4 6027
 
3.4%
Other values (6) 24628
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62005
35.0%
a 15529
 
8.8%
2 14350
 
8.1%
f 11783
 
6.7%
7 10142
 
5.7%
5 9317
 
5.3%
6 8434
 
4.8%
3 8155
 
4.6%
1 6774
 
3.8%
4 6027
 
3.4%
Other values (6) 24628
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62005
35.0%
a 15529
 
8.8%
2 14350
 
8.1%
f 11783
 
6.7%
7 10142
 
5.7%
5 9317
 
5.3%
6 8434
 
4.8%
3 8155
 
4.6%
1 6774
 
3.8%
4 6027
 
3.4%
Other values (6) 24628
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62005
35.0%
a 15529
 
8.8%
2 14350
 
8.1%
f 11783
 
6.7%
7 10142
 
5.7%
5 9317
 
5.3%
6 8434
 
4.8%
3 8155
 
4.6%
1 6774
 
3.8%
4 6027
 
3.4%
Other values (6) 24628
 
13.9%

purchasedItemCount
Real number (ℝ)

High correlation  Missing 

Distinct50
Distinct (%)0.7%
Missing484
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean240.76468
Minimum0
Maximum689
Zeros25
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:03.162964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q193
median167
Q3335
95-th percentile689
Maximum689
Range689
Interquartile range (IQR)242

Descriptive statistics

Standard deviation221.80545
Coefficient of variation (CV)0.92125412
Kurtosis-0.29312694
Mean240.76468
Median Absolute Deviation (MAD)136
Skewness0.96073798
Sum1660554
Variance49197.659
MonotonicityNot monotonic
2025-02-04T05:02:03.508162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
689 459
 
6.2%
670 450
 
6.1%
599 381
 
5.2%
5 370
 
5.0%
1 325
 
4.4%
133 248
 
3.4%
303 217
 
2.9%
2 212
 
2.9%
214 203
 
2.8%
212 194
 
2.6%
Other values (40) 3838
52.0%
(Missing) 484
 
6.6%
ValueCountFrequency (%)
0 25
 
0.3%
1 325
4.4%
2 212
2.9%
3 32
 
0.4%
4 157
2.1%
5 370
5.0%
6 23
 
0.3%
7 15
 
0.2%
8 2
 
< 0.1%
9 90
 
1.2%
ValueCountFrequency (%)
689 459
6.2%
670 450
6.1%
599 381
5.2%
348 127
 
1.7%
341 124
 
1.7%
335 185
2.5%
309 183
 
2.5%
303 217
2.9%
240 148
 
2.0%
229 99
 
1.3%
Distinct1107
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
Minimum2020-10-30 20:17:59
Maximum2021-03-01 23:17:34.772000
Invalid dates0
Invalid dates (%)0.0%
2025-02-04T05:02:03.859933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:02:04.236333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1107
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
Minimum2020-10-30 20:17:59
Maximum2021-03-01 23:17:34.772000
Invalid dates0
Invalid dates (%)0.0%
2025-02-04T05:02:04.534893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:02:04.793355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

finisheddate
Date

Missing 

Distinct553
Distinct (%)9.3%
Missing1411
Missing (%)19.1%
Memory size57.8 KiB
Minimum2021-01-03 15:24:10
Maximum2021-02-26 22:36:25
Invalid dates0
Invalid dates (%)0.0%
2025-02-04T05:02:04.992336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:02:05.199180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1104
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
Minimum2021-01-03 15:24:10
Maximum2021-03-01 23:17:34.772000
Invalid dates0
Invalid dates (%)0.0%
2025-02-04T05:02:05.393054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:02:05.617539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pointsawardeddate
Date

Missing 

Distinct523
Distinct (%)8.6%
Missing1301
Missing (%)17.6%
Memory size57.8 KiB
Minimum2020-10-30 20:18:00
Maximum2021-02-26 22:36:25
Invalid dates0
Invalid dates (%)0.0%
2025-02-04T05:02:05.809377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:02:06.051906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

purchasedate
Date

Missing 

Distinct358
Distinct (%)5.2%
Missing458
Missing (%)6.2%
Memory size57.8 KiB
Minimum2017-10-30 00:00:00
Maximum2021-03-08 17:37:13
Invalid dates0
Invalid dates (%)0.0%
2025-02-04T05:02:06.261103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:02:06.484897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_receipt_points_earned
Real number (ℝ)

High correlation  Missing 

Distinct119
Distinct (%)1.9%
Missing1128
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean2175.5837
Minimum0
Maximum10199.8
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size29.0 KiB
2025-02-04T05:02:06.697855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q1750
median1447
Q32685.8
95-th percentile7137.2002
Maximum10199.8
Range10199.8
Interquartile range (IQR)1935.8

Descriptive statistics

Standard deviation2175.7334
Coefficient of variation (CV)1.0000688
Kurtosis0.76142293
Mean2175.5837
Median Absolute Deviation (MAD)841.29999
Skewness1.3340405
Sum13603925
Variance4733815.5
MonotonicityNot monotonic
2025-02-04T05:02:06.925357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4944.700195 459
 
6.2%
7137.200195 450
 
6.1%
750 342
 
4.6%
5 298
 
4.0%
1178.699951 203
 
2.8%
922.0999756 194
 
2.6%
1658.300049 185
 
2.5%
3659.399902 183
 
2.5%
1135.099976 176
 
2.4%
487.7000122 155
 
2.1%
Other values (109) 3608
48.9%
(Missing) 1128
 
15.3%
ValueCountFrequency (%)
0 6
 
0.1%
5 298
4.0%
25 104
 
1.4%
35 7
 
0.1%
50 27
 
0.4%
50.59999847 5
 
0.1%
50.90000153 1
 
< 0.1%
55 22
 
0.3%
91.19999695 8
 
0.1%
94.59999847 5
 
0.1%
ValueCountFrequency (%)
10199.7998 2
 
< 0.1%
9850 1
 
< 0.1%
9449.799805 11
 
0.1%
9200 10
 
0.1%
8950 10
 
0.1%
8850 10
 
0.1%
8700 30
 
0.4%
7137.200195 450
6.1%
6257.299805 127
 
1.7%
5850 2
 
< 0.1%

total_receipt_spent
Real number (ℝ)

High correlation  Missing 

Distinct94
Distinct (%)1.4%
Missing435
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean1368.5772
Minimum0
Maximum4721.9502
Zeros25
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size29.0 KiB
2025-02-04T05:02:07.126737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1373.54999
median776.78998
Q31183.1
95-th percentile4721.9502
Maximum4721.9502
Range4721.9502
Interquartile range (IQR)809.54999

Descriptive statistics

Standard deviation1588.9594
Coefficient of variation (CV)1.1610301
Kurtosis0.20020902
Mean1368.5772
Median Absolute Deviation (MAD)406.31
Skewness1.3484646
Sum9506137.3
Variance2524791.8
MonotonicityNot monotonic
2025-02-04T05:02:07.335168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4566.169922 459
 
6.2%
4721.950195 450
 
6.1%
4368.799805 381
 
5.2%
1 324
 
4.4%
2084.820068 217
 
2.9%
49.95000076 215
 
2.9%
1183.099976 203
 
2.8%
743.789978 194
 
2.6%
1177.839966 185
 
2.5%
1043.180054 183
 
2.5%
Other values (84) 4135
56.0%
(Missing) 435
 
5.9%
ValueCountFrequency (%)
0 25
 
0.3%
0.1599999964 1
 
< 0.1%
0.9900000095 7
 
0.1%
1 324
4.4%
2 8
 
0.1%
2.230000019 7
 
0.1%
2.289999962 1
 
< 0.1%
2.99000001 1
 
< 0.1%
3 18
 
0.2%
3.089999914 5
 
0.1%
ValueCountFrequency (%)
4721.950195 450
6.1%
4566.169922 459
6.2%
4368.799805 381
5.2%
2084.820068 217
2.9%
1198.680054 127
 
1.7%
1183.099976 203
2.8%
1177.839966 185
2.5%
1107.819946 125
 
1.7%
1083.23999 124
 
1.7%
1043.180054 183
 
2.5%

barcode
Text

Missing 

Distinct568
Distinct (%)18.4%
Missing4291
Missing (%)58.1%
Memory size57.8 KiB
2025-02-04T05:02:07.661827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.067314
Min length2

Characters and Unicode

Total characters34198
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)8.4%

Sample

1st row021000051113
2nd row021000051113
3rd row021000051113
4th row021000051113
5th row043000204399
ValueCountFrequency (%)
4011 177
 
5.7%
036000320893 92
 
3.0%
034100573065 90
 
2.9%
036000391718 87
 
2.8%
012000809941 76
 
2.5%
076840580750 63
 
2.0%
041000022623 54
 
1.7%
076840100354 53
 
1.7%
028400642033 45
 
1.5%
311111511867 41
 
1.3%
Other values (558) 2312
74.8%
2025-02-04T05:02:08.073497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

description
Text

Missing 

Distinct1889
Distinct (%)28.8%
Missing821
Missing (%)11.1%
Memory size57.8 KiB
2025-02-04T05:02:08.399141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length155
Median length92
Mean length29.15122
Min length2

Characters and Unicode

Total characters191232
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1136 ?
Unique (%)17.3%

Sample

1st rowMSSN TORTLLA
2nd rowJell-O Instant Pudding & Pie Filling French Vanilla, 3.4 Oz
3rd rowITEM NOT FOUND
4th rowVASELINE COCOA BUTTER SKIN MOISTURIZER LOTION CONDITIONING RP 34.5 OZ
5th rowITEM NOT FOUND
ValueCountFrequency (%)
oz 1209
 
3.5%
931
 
2.7%
cheese 327
 
1.0%
12 321
 
0.9%
bag 276
 
0.8%
hyv 246
 
0.7%
can 241
 
0.7%
ct 237
 
0.7%
regular 215
 
0.6%
fl 211
 
0.6%
Other values (3183) 30149
87.7%
2025-02-04T05:02:08.989589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

finalPrice
Real number (ℝ)

High correlation  Missing 

Distinct823
Distinct (%)12.2%
Missing614
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean7.871661
Minimum0
Maximum441.58
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:09.131320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.56
Q12.29
median4.28
Q39.99
95-th percentile26
Maximum441.58
Range441.58
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation14.656776
Coefficient of variation (CV)1.8619674
Kurtosis207.38946
Mean7.871661
Median Absolute Deviation (MAD)2.7
Skewness11.383034
Sum53267.53
Variance214.82108
MonotonicityNot monotonic
2025-02-04T05:02:09.320792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 375
 
5.1%
9.99 355
 
4.8%
3.99 243
 
3.3%
4.99 195
 
2.6%
0.56 190
 
2.6%
2.99 179
 
2.4%
5.99 176
 
2.4%
3.49 139
 
1.9%
2.34 134
 
1.8%
5 124
 
1.7%
Other values (813) 4657
63.1%
(Missing) 614
 
8.3%
ValueCountFrequency (%)
0 4
 
0.1%
0.16 1
 
< 0.1%
0.19 13
 
0.2%
0.25 2
 
< 0.1%
0.32 2
 
< 0.1%
0.48 3
 
< 0.1%
0.5 76
 
1.0%
0.54 66
 
0.9%
0.55 2
 
< 0.1%
0.56 190
2.6%
ValueCountFrequency (%)
441.58 1
 
< 0.1%
245 3
< 0.1%
223.36 5
0.1%
180 6
0.1%
168.84 5
0.1%
115.96 1
 
< 0.1%
100.48 1
 
< 0.1%
100 6
0.1%
95.84 4
0.1%
82.34 1
 
< 0.1%

itemPrice
Real number (ℝ)

High correlation  Missing 

Distinct823
Distinct (%)12.2%
Missing614
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean7.8721782
Minimum0
Maximum441.58
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:09.507784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.56
Q12.29
median4.28
Q39.99
95-th percentile26
Maximum441.58
Range441.58
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation14.656623
Coefficient of variation (CV)1.8618256
Kurtosis207.39662
Mean7.8721782
Median Absolute Deviation (MAD)2.7
Skewness11.383294
Sum53271.03
Variance214.8166
MonotonicityNot monotonic
2025-02-04T05:02:09.718547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 375
 
5.1%
9.99 355
 
4.8%
3.99 243
 
3.3%
4.99 196
 
2.7%
0.56 190
 
2.6%
2.99 180
 
2.4%
5.99 176
 
2.4%
3.49 139
 
1.9%
2.34 134
 
1.8%
5 124
 
1.7%
Other values (813) 4655
63.1%
(Missing) 614
 
8.3%
ValueCountFrequency (%)
0 4
 
0.1%
0.16 1
 
< 0.1%
0.19 13
 
0.2%
0.25 2
 
< 0.1%
0.32 2
 
< 0.1%
0.48 3
 
< 0.1%
0.5 76
 
1.0%
0.54 66
 
0.9%
0.55 2
 
< 0.1%
0.56 190
2.6%
ValueCountFrequency (%)
441.58 1
 
< 0.1%
245 3
< 0.1%
223.36 5
0.1%
180 6
0.1%
168.84 5
0.1%
115.96 1
 
< 0.1%
100.48 1
 
< 0.1%
100 6
0.1%
95.84 4
0.1%
82.34 1
 
< 0.1%

needsFetchReview
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.2%
Missing6568
Missing (%)89.0%
Memory size57.8 KiB
False
 
594
True
 
219
(Missing)
6568 
ValueCountFrequency (%)
False 594
 
8.0%
True 219
 
3.0%
(Missing) 6568
89.0%
2025-02-04T05:02:09.848710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

partnerItemId
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct916
Distinct (%)13.2%
Missing440
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean988.52428
Minimum0
Maximum2043
Zeros145
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:09.996168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11027
median1143
Q31274
95-th percentile1644
Maximum2043
Range2043
Interquartile range (IQR)247

Descriptive statistics

Standard deviation527.38082
Coefficient of variation (CV)0.53350316
Kurtosis-0.13640789
Mean988.52428
Median Absolute Deviation (MAD)123
Skewness-1.0174858
Sum6861347
Variance278130.53
MonotonicityNot monotonic
2025-02-04T05:02:10.211456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 531
 
7.2%
2 203
 
2.8%
0 145
 
2.0%
3 135
 
1.8%
4 113
 
1.5%
5 106
 
1.4%
7 34
 
0.5%
8 34
 
0.5%
6 34
 
0.5%
9 34
 
0.5%
Other values (906) 5572
75.5%
(Missing) 440
 
6.0%
ValueCountFrequency (%)
0 145
 
2.0%
1 531
7.2%
2 203
 
2.8%
3 135
 
1.8%
4 113
 
1.5%
5 106
 
1.4%
6 34
 
0.5%
7 34
 
0.5%
8 34
 
0.5%
9 34
 
0.5%
ValueCountFrequency (%)
2043 1
< 0.1%
2040 1
< 0.1%
2036 1
< 0.1%
2033 1
< 0.1%
2029 1
< 0.1%
2026 1
< 0.1%
1986 1
< 0.1%
1983 1
< 0.1%
1980 1
< 0.1%
1976 1
< 0.1%

preventTargetGapPoints
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing7023
Missing (%)95.1%
Memory size57.8 KiB
True
 
358
(Missing)
7023 
ValueCountFrequency (%)
True 358
 
4.9%
(Missing) 7023
95.1%
2025-02-04T05:02:10.347606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

quantityPurchased
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)0.2%
Missing614
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean1.3861386
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:10.428842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum17
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2043632
Coefficient of variation (CV)0.86886201
Kurtosis36.002117
Mean1.3861386
Median Absolute Deviation (MAD)0
Skewness5.1137362
Sum9380
Variance1.4504907
MonotonicityNot monotonic
2025-02-04T05:02:10.573506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 5628
76.2%
2 622
 
8.4%
4 170
 
2.3%
3 134
 
1.8%
5 101
 
1.4%
6 37
 
0.5%
8 22
 
0.3%
10 15
 
0.2%
7 13
 
0.2%
9 13
 
0.2%
Other values (3) 12
 
0.2%
(Missing) 614
 
8.3%
ValueCountFrequency (%)
1 5628
76.2%
2 622
 
8.4%
3 134
 
1.8%
4 170
 
2.3%
5 101
 
1.4%
6 37
 
0.5%
7 13
 
0.2%
8 22
 
0.3%
9 13
 
0.2%
10 15
 
0.2%
ValueCountFrequency (%)
17 3
 
< 0.1%
14 3
 
< 0.1%
12 6
 
0.1%
10 15
 
0.2%
9 13
 
0.2%
8 22
 
0.3%
7 13
 
0.2%
6 37
 
0.5%
5 101
1.4%
4 170
2.3%

userFlaggedBarcode
Categorical

High correlation  Missing 

Distinct6
Distinct (%)1.8%
Missing7044
Missing (%)95.4%
Memory size57.8 KiB
034100573065
166 
4011
107 
1234
32 
028400642255
 
13
079400066619
 
10

Length

Max length12
Median length12
Mean length8.7002967
Min length4

Characters and Unicode

Total characters2932
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1234
2nd row034100573065
3rd row034100573065
4th row034100573065
5th row034100573065

Common Values

ValueCountFrequency (%)
034100573065 166
 
2.2%
4011 107
 
1.4%
1234 32
 
0.4%
028400642255 13
 
0.2%
079400066619 10
 
0.1%
075925306254 9
 
0.1%
(Missing) 7044
95.4%

Length

2025-02-04T05:02:10.754869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:10.876248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034100573065 166
49.3%
4011 107
31.8%
1234 32
 
9.5%
028400642255 13
 
3.9%
079400066619 10
 
3.0%
075925306254 9
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

userFlaggedNewItem
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing7058
Missing (%)95.6%
Memory size57.8 KiB
True
 
323
(Missing)
7058 
ValueCountFrequency (%)
True 323
 
4.4%
(Missing) 7058
95.6%
2025-02-04T05:02:10.980313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

userFlaggedPrice
Categorical

High correlation  Missing 

Distinct13
Distinct (%)4.3%
Missing7082
Missing (%)95.9%
Memory size57.8 KiB
29.00
142 
1.00
26 
10.00
22 
28.00
15 
21.00
 
14
Other values (8)
80 

Length

Max length5
Median length5
Mean length4.8862876
Min length4

Characters and Unicode

Total characters1461
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.00
2nd row29.00
3rd row29.00
4th row29.00
5th row29.00

Common Values

ValueCountFrequency (%)
29.00 142
 
1.9%
1.00 26
 
0.4%
10.00 22
 
0.3%
28.00 15
 
0.2%
21.00 14
 
0.2%
20.00 14
 
0.2%
27.00 13
 
0.2%
25.00 10
 
0.1%
23.00 10
 
0.1%
26.00 10
 
0.1%
Other values (3) 23
 
0.3%
(Missing) 7082
95.9%

Length

2025-02-04T05:02:11.084834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29.00 142
47.5%
1.00 26
 
8.7%
10.00 22
 
7.4%
28.00 15
 
5.0%
21.00 14
 
4.7%
20.00 14
 
4.7%
27.00 13
 
4.3%
25.00 10
 
3.3%
23.00 10
 
3.3%
26.00 10
 
3.3%
Other values (3) 23
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
5 18
 
1.2%
6 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
5 18
 
1.2%
6 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
5 18
 
1.2%
6 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
5 18
 
1.2%
6 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

userFlaggedQuantity
Categorical

High correlation  Missing 

Distinct5
Distinct (%)1.7%
Missing7082
Missing (%)95.9%
Memory size57.8 KiB
1.0
189 
3.0
31 
4.0
30 
2.0
29 
5.0
20 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters897
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 189
 
2.6%
3.0 31
 
0.4%
4.0 30
 
0.4%
2.0 29
 
0.4%
5.0 20
 
0.3%
(Missing) 7082
95.9%

Length

2025-02-04T05:02:11.228084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:11.341407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 189
63.2%
3.0 31
 
10.4%
4.0 30
 
10.0%
2.0 29
 
9.7%
5.0 20
 
6.7%

Most occurring characters

ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

needsFetchReviewReason
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.9%
Missing7162
Missing (%)97.0%
Memory size57.8 KiB
USER_FLAGGED
211 
POINTS_GREATER_THAN_THRESHOLD
 
8

Length

Max length29
Median length12
Mean length12.621005
Min length12

Characters and Unicode

Total characters2764
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSER_FLAGGED
2nd rowUSER_FLAGGED
3rd rowUSER_FLAGGED
4th rowUSER_FLAGGED
5th rowUSER_FLAGGED

Common Values

ValueCountFrequency (%)
USER_FLAGGED 211
 
2.9%
POINTS_GREATER_THAN_THRESHOLD 8
 
0.1%
(Missing) 7162
97.0%

Length

2025-02-04T05:02:11.496866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:11.593513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
user_flagged 211
96.3%
points_greater_than_threshold 8
 
3.7%

Most occurring characters

ValueCountFrequency (%)
E 446
16.1%
G 430
15.6%
R 235
8.5%
_ 235
8.5%
S 227
8.2%
A 227
8.2%
L 219
7.9%
D 219
7.9%
U 211
7.6%
F 211
7.6%
Other values (6) 104
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 446
16.1%
G 430
15.6%
R 235
8.5%
_ 235
8.5%
S 227
8.2%
A 227
8.2%
L 219
7.9%
D 219
7.9%
U 211
7.6%
F 211
7.6%
Other values (6) 104
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 446
16.1%
G 430
15.6%
R 235
8.5%
_ 235
8.5%
S 227
8.2%
A 227
8.2%
L 219
7.9%
D 219
7.9%
U 211
7.6%
F 211
7.6%
Other values (6) 104
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 446
16.1%
G 430
15.6%
R 235
8.5%
_ 235
8.5%
S 227
8.2%
A 227
8.2%
L 219
7.9%
D 219
7.9%
U 211
7.6%
F 211
7.6%
Other values (6) 104
 
3.8%

pointsNotAwardedReason
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing7041
Missing (%)95.4%
Memory size57.8 KiB
Action not allowed for user and CPG
340 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters11900
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAction not allowed for user and CPG
2nd rowAction not allowed for user and CPG
3rd rowAction not allowed for user and CPG
4th rowAction not allowed for user and CPG
5th rowAction not allowed for user and CPG

Common Values

ValueCountFrequency (%)
Action not allowed for user and CPG 340
 
4.6%
(Missing) 7041
95.4%

Length

2025-02-04T05:02:11.732813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:11.825640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
action 340
14.3%
not 340
14.3%
allowed 340
14.3%
for 340
14.3%
user 340
14.3%
and 340
14.3%
cpg 340
14.3%

Most occurring characters

ValueCountFrequency (%)
2040
17.1%
o 1360
11.4%
n 1020
 
8.6%
t 680
 
5.7%
a 680
 
5.7%
l 680
 
5.7%
e 680
 
5.7%
d 680
 
5.7%
r 680
 
5.7%
A 340
 
2.9%
Other values (9) 3060
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2040
17.1%
o 1360
11.4%
n 1020
 
8.6%
t 680
 
5.7%
a 680
 
5.7%
l 680
 
5.7%
e 680
 
5.7%
d 680
 
5.7%
r 680
 
5.7%
A 340
 
2.9%
Other values (9) 3060
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2040
17.1%
o 1360
11.4%
n 1020
 
8.6%
t 680
 
5.7%
a 680
 
5.7%
l 680
 
5.7%
e 680
 
5.7%
d 680
 
5.7%
r 680
 
5.7%
A 340
 
2.9%
Other values (9) 3060
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2040
17.1%
o 1360
11.4%
n 1020
 
8.6%
t 680
 
5.7%
a 680
 
5.7%
l 680
 
5.7%
e 680
 
5.7%
d 680
 
5.7%
r 680
 
5.7%
A 340
 
2.9%
Other values (9) 3060
25.7%

pointsPayerId
Categorical

High correlation  Missing 

Distinct15
Distinct (%)1.2%
Missing6114
Missing (%)82.8%
Memory size57.8 KiB
559c2234e4b06aca36af13c6
331 
5332f5f6e4b03c9a25efd0b4
281 
5332f5fbe4b03c9a25efd0ba
230 
550b2565e4b001d5e9e4146f
190 
5332f709e4b03c9a25efd0f1
90 
Other values (10)
145 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters30408
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st row5332f5f6e4b03c9a25efd0b4
2nd row5332f5f3e4b03c9a25efd0ae
3rd row5332f5f6e4b03c9a25efd0b4
4th row5332f5fbe4b03c9a25efd0ba
5th row5332f5fbe4b03c9a25efd0ba

Common Values

ValueCountFrequency (%)
559c2234e4b06aca36af13c6 331
 
4.5%
5332f5f6e4b03c9a25efd0b4 281
 
3.8%
5332f5fbe4b03c9a25efd0ba 230
 
3.1%
550b2565e4b001d5e9e4146f 190
 
2.6%
5332f709e4b03c9a25efd0f1 90
 
1.2%
5332f5f3e4b03c9a25efd0ae 56
 
0.8%
5a734034e4b0d58f376be874 31
 
0.4%
5e825d64f221c312e698a62a 25
 
0.3%
5eebc5412455c97a877ef382 24
 
0.3%
53e10d6368abd3c7065097cc 3
 
< 0.1%
Other values (5) 6
 
0.1%
(Missing) 6114
82.8%

Length

2025-02-04T05:02:11.947538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
559c2234e4b06aca36af13c6 331
26.1%
5332f5f6e4b03c9a25efd0b4 281
22.2%
5332f5fbe4b03c9a25efd0ba 230
18.2%
550b2565e4b001d5e9e4146f 190
15.0%
5332f709e4b03c9a25efd0f1 90
 
7.1%
5332f5f3e4b03c9a25efd0ae 56
 
4.4%
5a734034e4b0d58f376be874 31
 
2.4%
5e825d64f221c312e698a62a 25
 
2.0%
5eebc5412455c97a877ef382 24
 
1.9%
53e10d6368abd3c7065097cc 3
 
0.2%
Other values (5) 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
5 3716
12.2%
3 3180
10.5%
f 2586
8.5%
e 2470
8.1%
0 2384
7.8%
4 2369
7.8%
2 2352
7.7%
b 2205
7.3%
a 2049
 
6.7%
6 1780
 
5.9%
Other values (6) 5317
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 3716
12.2%
3 3180
10.5%
f 2586
8.5%
e 2470
8.1%
0 2384
7.8%
4 2369
7.8%
2 2352
7.7%
b 2205
7.3%
a 2049
 
6.7%
6 1780
 
5.9%
Other values (6) 5317
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 3716
12.2%
3 3180
10.5%
f 2586
8.5%
e 2470
8.1%
0 2384
7.8%
4 2369
7.8%
2 2352
7.7%
b 2205
7.3%
a 2049
 
6.7%
6 1780
 
5.9%
Other values (6) 5317
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 3716
12.2%
3 3180
10.5%
f 2586
8.5%
e 2470
8.1%
0 2384
7.8%
4 2369
7.8%
2 2352
7.7%
b 2205
7.3%
a 2049
 
6.7%
6 1780
 
5.9%
Other values (6) 5317
17.5%

rewardsGroup
Text

Missing 

Distinct182
Distinct (%)10.5%
Missing5650
Missing (%)76.5%
Memory size57.8 KiB
2025-02-04T05:02:12.332728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length45
Mean length30.23859
Min length7

Characters and Unicode

Total characters52343
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)4.5%

Sample

1st rowSARGENTO GRATED PARMESAN CHEESE
2nd rowSARGENTO GRATED PARMESAN CHEESE
3rd rowSARGENTO GRATED PARMESAN CHEESE
4th rowSARGENTO GRATED PARMESAN CHEESE
5th rowVASELINE HAND AND BODY LOTION
ValueCountFrequency (%)
and 306
 
3.4%
cheese 296
 
3.3%
pack 279
 
3.1%
249
 
2.7%
count 221
 
2.4%
or 215
 
2.4%
larger 207
 
2.3%
12 193
 
2.1%
ice 184
 
2.0%
cream 184
 
2.0%
Other values (386) 6768
74.4%
2025-02-04T05:02:12.930284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7371
14.1%
E 6109
 
11.7%
A 3592
 
6.9%
R 3561
 
6.8%
S 3452
 
6.6%
I 2772
 
5.3%
N 2751
 
5.3%
C 2534
 
4.8%
O 2388
 
4.6%
L 2113
 
4.0%
Other values (36) 15700
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7371
14.1%
E 6109
 
11.7%
A 3592
 
6.9%
R 3561
 
6.8%
S 3452
 
6.6%
I 2772
 
5.3%
N 2751
 
5.3%
C 2534
 
4.8%
O 2388
 
4.6%
L 2113
 
4.0%
Other values (36) 15700
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7371
14.1%
E 6109
 
11.7%
A 3592
 
6.9%
R 3561
 
6.8%
S 3452
 
6.6%
I 2772
 
5.3%
N 2751
 
5.3%
C 2534
 
4.8%
O 2388
 
4.6%
L 2113
 
4.0%
Other values (36) 15700
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7371
14.1%
E 6109
 
11.7%
A 3592
 
6.9%
R 3561
 
6.8%
S 3452
 
6.6%
I 2772
 
5.3%
N 2751
 
5.3%
C 2534
 
4.8%
O 2388
 
4.6%
L 2113
 
4.0%
Other values (36) 15700
30.0%

rewardsProductPartnerId
Categorical

High correlation  Missing 

Distinct16
Distinct (%)0.7%
Missing5112
Missing (%)69.3%
Memory size57.8 KiB
559c2234e4b06aca36af13c6
878 
5332f5f6e4b03c9a25efd0b4
315 
5332f5fbe4b03c9a25efd0ba
243 
550b2565e4b001d5e9e4146f
202 
5332f5f3e4b03c9a25efd0ae
181 
Other values (11)
450 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters54456
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row5e7cf838f221c312e698a628
2nd row5e7cf838f221c312e698a628
3rd row5e7cf838f221c312e698a628
4th row5e7cf838f221c312e698a628
5th row559c2234e4b06aca36af13c6

Common Values

ValueCountFrequency (%)
559c2234e4b06aca36af13c6 878
 
11.9%
5332f5f6e4b03c9a25efd0b4 315
 
4.3%
5332f5fbe4b03c9a25efd0ba 243
 
3.3%
550b2565e4b001d5e9e4146f 202
 
2.7%
5332f5f3e4b03c9a25efd0ae 181
 
2.5%
5e7cf838f221c312e698a628 157
 
2.1%
5e825d64f221c312e698a62a 124
 
1.7%
5332f709e4b03c9a25efd0f1 92
 
1.2%
5a734034e4b0d58f376be874 31
 
0.4%
5eebc5412455c97a877ef382 24
 
0.3%
Other values (6) 22
 
0.3%
(Missing) 5112
69.3%

Length

2025-02-04T05:02:13.077718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
559c2234e4b06aca36af13c6 878
38.7%
5332f5f6e4b03c9a25efd0b4 315
 
13.9%
5332f5fbe4b03c9a25efd0ba 243
 
10.7%
550b2565e4b001d5e9e4146f 202
 
8.9%
5332f5f3e4b03c9a25efd0ae 181
 
8.0%
5e7cf838f221c312e698a628 157
 
6.9%
5e825d64f221c312e698a62a 124
 
5.5%
5332f709e4b03c9a25efd0f1 92
 
4.1%
5a734034e4b0d58f376be874 31
 
1.4%
5eebc5412455c97a877ef382 24
 
1.1%
Other values (6) 22
 
1.0%

Most occurring characters

ValueCountFrequency (%)
3 5885
10.8%
5 5784
10.6%
2 4971
9.1%
a 4393
8.1%
6 4111
7.5%
f 4093
7.5%
e 4049
7.4%
c 3988
 
7.3%
4 3824
 
7.0%
0 3324
 
6.1%
Other values (6) 10034
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 5885
10.8%
5 5784
10.6%
2 4971
9.1%
a 4393
8.1%
6 4111
7.5%
f 4093
7.5%
e 4049
7.4%
c 3988
 
7.3%
4 3824
 
7.0%
0 3324
 
6.1%
Other values (6) 10034
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 5885
10.8%
5 5784
10.6%
2 4971
9.1%
a 4393
8.1%
6 4111
7.5%
f 4093
7.5%
e 4049
7.4%
c 3988
 
7.3%
4 3824
 
7.0%
0 3324
 
6.1%
Other values (6) 10034
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 5885
10.8%
5 5784
10.6%
2 4971
9.1%
a 4393
8.1%
6 4111
7.5%
f 4093
7.5%
e 4049
7.4%
c 3988
 
7.3%
4 3824
 
7.0%
0 3324
 
6.1%
Other values (6) 10034
18.4%

userFlaggedDescription
Categorical

High correlation  Missing 

Distinct3
Distinct (%)1.5%
Missing7176
Missing (%)97.2%
Memory size57.8 KiB
MILLER LITE 24 PACK 12OZ CAN
147 
51 
DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ
 
7

Length

Max length60
Median length28
Mean length22.126829
Min length0

Characters and Unicode

Total characters4536
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd rowMILLER LITE 24 PACK 12OZ CAN
3rd rowMILLER LITE 24 PACK 12OZ CAN
4th rowMILLER LITE 24 PACK 12OZ CAN
5th rowMILLER LITE 24 PACK 12OZ CAN

Common Values

ValueCountFrequency (%)
MILLER LITE 24 PACK 12OZ CAN 147
 
2.0%
51
 
0.7%
DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ 7
 
0.1%
(Missing) 7176
97.2%

Length

2025-02-04T05:02:13.248057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:13.359698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
miller 147
15.3%
24 147
15.3%
pack 147
15.3%
12oz 147
15.3%
can 147
15.3%
lite 147
15.3%
chili 7
 
0.7%
1 7
 
0.7%
bag 7
 
0.7%
fat 7
 
0.7%
Other values (7) 49
 
5.1%

Most occurring characters

ValueCountFrequency (%)
805
17.7%
L 462
 
10.2%
I 336
 
7.4%
E 322
 
7.1%
C 322
 
7.1%
A 315
 
6.9%
2 294
 
6.5%
T 182
 
4.0%
O 175
 
3.9%
R 168
 
3.7%
Other values (16) 1155
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
805
17.7%
L 462
 
10.2%
I 336
 
7.4%
E 322
 
7.1%
C 322
 
7.1%
A 315
 
6.9%
2 294
 
6.5%
T 182
 
4.0%
O 175
 
3.9%
R 168
 
3.7%
Other values (16) 1155
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
805
17.7%
L 462
 
10.2%
I 336
 
7.4%
E 322
 
7.1%
C 322
 
7.1%
A 315
 
6.9%
2 294
 
6.5%
T 182
 
4.0%
O 175
 
3.9%
R 168
 
3.7%
Other values (16) 1155
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
805
17.7%
L 462
 
10.2%
I 336
 
7.4%
E 322
 
7.1%
C 322
 
7.1%
A 315
 
6.9%
2 294
 
6.5%
T 182
 
4.0%
O 175
 
3.9%
R 168
 
3.7%
Other values (16) 1155
25.5%

originalMetaBriteBarcode
Categorical

High correlation  Missing 

Distinct6
Distinct (%)8.5%
Missing7310
Missing (%)99.0%
Memory size57.8 KiB
47 
080878042197
10 
034100573065
028400642255
 
4
075925306254
 
3

Length

Max length12
Median length0
Mean length4.056338
Min length0

Characters and Unicode

Total characters288
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row028400642255
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
47
 
0.6%
080878042197 10
 
0.1%
034100573065 6
 
0.1%
028400642255 4
 
0.1%
075925306254 3
 
< 0.1%
002900001903 1
 
< 0.1%
(Missing) 7310
99.0%

Length

2025-02-04T05:02:13.492898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:13.606359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
080878042197 10
41.7%
034100573065 6
25.0%
028400642255 4
 
16.7%
075925306254 3
 
12.5%
002900001903 1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 79
27.4%
8 34
11.8%
7 29
 
10.1%
2 29
 
10.1%
5 29
 
10.1%
4 27
 
9.4%
1 17
 
5.9%
3 16
 
5.6%
9 15
 
5.2%
6 13
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79
27.4%
8 34
11.8%
7 29
 
10.1%
2 29
 
10.1%
5 29
 
10.1%
4 27
 
9.4%
1 17
 
5.9%
3 16
 
5.6%
9 15
 
5.2%
6 13
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79
27.4%
8 34
11.8%
7 29
 
10.1%
2 29
 
10.1%
5 29
 
10.1%
4 27
 
9.4%
1 17
 
5.9%
3 16
 
5.6%
9 15
 
5.2%
6 13
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79
27.4%
8 34
11.8%
7 29
 
10.1%
2 29
 
10.1%
5 29
 
10.1%
4 27
 
9.4%
1 17
 
5.9%
3 16
 
5.6%
9 15
 
5.2%
6 13
 
4.5%

originalMetaBriteDescription
Categorical

High correlation  Missing 

Distinct2
Distinct (%)20.0%
Missing7371
Missing (%)99.9%
Memory size57.8 KiB
MILLER LITE 24 PACK 12OZ CAN
DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ

Length

Max length60
Median length28
Mean length40.8
Min length28

Characters and Unicode

Total characters408
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ
2nd rowMILLER LITE 24 PACK 12OZ CAN
3rd rowMILLER LITE 24 PACK 12OZ CAN
4th rowDORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ
5th rowDORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ

Common Values

ValueCountFrequency (%)
MILLER LITE 24 PACK 12OZ CAN 6
 
0.1%
DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ 4
 
0.1%
(Missing) 7371
99.9%

Length

2025-02-04T05:02:13.788182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:13.889086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
miller 6
 
7.5%
24 6
 
7.5%
pack 6
 
7.5%
12oz 6
 
7.5%
can 6
 
7.5%
lite 6
 
7.5%
chili 4
 
5.0%
1 4
 
5.0%
bag 4
 
5.0%
fat 4
 
5.0%
Other values (7) 28
35.0%

Most occurring characters

ValueCountFrequency (%)
70
17.2%
I 36
 
8.8%
L 30
 
7.4%
E 28
 
6.9%
C 28
 
6.9%
T 26
 
6.4%
A 24
 
5.9%
O 22
 
5.4%
R 18
 
4.4%
P 14
 
3.4%
Other values (16) 112
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70
17.2%
I 36
 
8.8%
L 30
 
7.4%
E 28
 
6.9%
C 28
 
6.9%
T 26
 
6.4%
A 24
 
5.9%
O 22
 
5.4%
R 18
 
4.4%
P 14
 
3.4%
Other values (16) 112
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70
17.2%
I 36
 
8.8%
L 30
 
7.4%
E 28
 
6.9%
C 28
 
6.9%
T 26
 
6.4%
A 24
 
5.9%
O 22
 
5.4%
R 18
 
4.4%
P 14
 
3.4%
Other values (16) 112
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70
17.2%
I 36
 
8.8%
L 30
 
7.4%
E 28
 
6.9%
C 28
 
6.9%
T 26
 
6.4%
A 24
 
5.9%
O 22
 
5.4%
R 18
 
4.4%
P 14
 
3.4%
Other values (16) 112
27.5%

brandCode
Text

Missing 

Distinct227
Distinct (%)8.7%
Missing4781
Missing (%)64.8%
Memory size57.8 KiB
2025-02-04T05:02:14.163211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length23
Mean length9.24
Min length2

Characters and Unicode

Total characters24024
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)2.2%

Sample

1st rowMISSION
2nd rowBRAND
3rd rowTIC TAC
4th rowWONDERFUL
5th rowRICE-A-RONI
ValueCountFrequency (%)
hy-vee 299
 
7.5%
ben 180
 
4.5%
and 180
 
4.5%
jerrys 180
 
4.5%
pepsi 93
 
2.3%
kroger 89
 
2.2%
kleenex 88
 
2.2%
knorr 79
 
2.0%
doritos 77
 
1.9%
borden 71
 
1.8%
Other values (308) 2632
66.3%
2025-02-04T05:02:14.648476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3252
13.5%
R 2016
 
8.4%
S 1594
 
6.6%
A 1433
 
6.0%
N 1424
 
5.9%
1368
 
5.7%
I 1289
 
5.4%
L 1232
 
5.1%
O 1194
 
5.0%
T 1102
 
4.6%
Other values (26) 8120
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 3252
13.5%
R 2016
 
8.4%
S 1594
 
6.6%
A 1433
 
6.0%
N 1424
 
5.9%
1368
 
5.7%
I 1289
 
5.4%
L 1232
 
5.1%
O 1194
 
5.0%
T 1102
 
4.6%
Other values (26) 8120
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 3252
13.5%
R 2016
 
8.4%
S 1594
 
6.6%
A 1433
 
6.0%
N 1424
 
5.9%
1368
 
5.7%
I 1289
 
5.4%
L 1232
 
5.1%
O 1194
 
5.0%
T 1102
 
4.6%
Other values (26) 8120
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 3252
13.5%
R 2016
 
8.4%
S 1594
 
6.6%
A 1433
 
6.0%
N 1424
 
5.9%
1368
 
5.7%
I 1289
 
5.4%
L 1232
 
5.1%
O 1194
 
5.0%
T 1102
 
4.6%
Other values (26) 8120
33.8%

competitorRewardsGroup
Categorical

High correlation  Missing 

Distinct30
Distinct (%)10.9%
Missing7106
Missing (%)96.3%
Memory size57.8 KiB
OSCAR MAYER SAUSAGE LINK
43 
MAXWELL HOUSE GROUND COFFEE
36 
A.1. DRY RUB
29 
OSCAR MAYER BACON
24 
TACO BELL TACO SHELLS
21 
Other values (25)
122 

Length

Max length47
Median length32
Mean length21.418182
Min length10

Characters and Unicode

Total characters5890
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)2.5%

Sample

1st rowTACO BELL TACO SHELLS
2nd rowPLANTERS PISTACHIOS
3rd rowSMART ONES
4th rowMAXWELL HOUSE GROUND COFFEE
5th rowFOOD NETWORK KITCHEN INSPIRATIONS COOKING SAUCE

Common Values

ValueCountFrequency (%)
OSCAR MAYER SAUSAGE LINK 43
 
0.6%
MAXWELL HOUSE GROUND COFFEE 36
 
0.5%
A.1. DRY RUB 29
 
0.4%
OSCAR MAYER BACON 24
 
0.3%
TACO BELL TACO SHELLS 21
 
0.3%
LIPTON TEA 20
 
0.3%
T.G.I. FRIDAY'S FROZEN APPETIZER 13
 
0.2%
CAPRI SUN BEVERAGE DRINK 13
 
0.2%
SMART ONES 11
 
0.1%
FOOD NETWORK KITCHEN INSPIRATIONS COOKING SAUCE 10
 
0.1%
Other values (20) 55
 
0.7%
(Missing) 7106
96.3%

Length

2025-02-04T05:02:14.890505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oscar 71
 
7.2%
mayer 71
 
7.2%
sausage 43
 
4.4%
link 43
 
4.4%
taco 42
 
4.3%
coffee 38
 
3.9%
maxwell 36
 
3.7%
house 36
 
3.7%
ground 36
 
3.7%
a.1 29
 
2.9%
Other values (77) 539
54.8%

Most occurring characters

ValueCountFrequency (%)
709
12.0%
A 562
 
9.5%
E 560
 
9.5%
O 415
 
7.0%
S 415
 
7.0%
R 393
 
6.7%
C 292
 
5.0%
N 276
 
4.7%
L 249
 
4.2%
I 248
 
4.2%
Other values (25) 1771
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
709
12.0%
A 562
 
9.5%
E 560
 
9.5%
O 415
 
7.0%
S 415
 
7.0%
R 393
 
6.7%
C 292
 
5.0%
N 276
 
4.7%
L 249
 
4.2%
I 248
 
4.2%
Other values (25) 1771
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
709
12.0%
A 562
 
9.5%
E 560
 
9.5%
O 415
 
7.0%
S 415
 
7.0%
R 393
 
6.7%
C 292
 
5.0%
N 276
 
4.7%
L 249
 
4.2%
I 248
 
4.2%
Other values (25) 1771
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
709
12.0%
A 562
 
9.5%
E 560
 
9.5%
O 415
 
7.0%
S 415
 
7.0%
R 393
 
6.7%
C 292
 
5.0%
N 276
 
4.7%
L 249
 
4.2%
I 248
 
4.2%
Other values (25) 1771
30.1%

discountedItemPrice
Real number (ℝ)

High correlation  Missing 

Distinct817
Distinct (%)14.2%
Missing1612
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean7.6643664
Minimum0.16
Maximum441.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:15.220556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.56
Q12.34
median3.99
Q37.96
95-th percentile22.97
Maximum441.58
Range441.42
Interquartile range (IQR)5.62

Descriptive statistics

Standard deviation15.463778
Coefficient of variation (CV)2.0176199
Kurtosis196.739
Mean7.6643664
Median Absolute Deviation (MAD)2.19
Skewness11.362894
Sum44215.73
Variance239.12844
MonotonicityNot monotonic
2025-02-04T05:02:15.589283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.99 243
 
3.3%
4.99 196
 
2.7%
0.56 190
 
2.6%
2.99 179
 
2.4%
5.99 176
 
2.4%
3.49 139
 
1.9%
2.34 134
 
1.8%
2.49 106
 
1.4%
1.99 98
 
1.3%
1 94
 
1.3%
Other values (807) 4214
57.1%
(Missing) 1612
 
21.8%
ValueCountFrequency (%)
0.16 1
 
< 0.1%
0.19 13
 
0.2%
0.25 2
 
< 0.1%
0.32 2
 
< 0.1%
0.48 3
 
< 0.1%
0.5 76
 
1.0%
0.54 66
 
0.9%
0.55 2
 
< 0.1%
0.56 190
2.6%
0.58 1
 
< 0.1%
ValueCountFrequency (%)
441.58 1
 
< 0.1%
245 3
< 0.1%
223.36 5
0.1%
180 6
0.1%
168.84 5
0.1%
115.96 1
 
< 0.1%
100.48 1
 
< 0.1%
100 6
0.1%
95.84 4
0.1%
82.34 1
 
< 0.1%
Distinct1738
Distinct (%)30.2%
Missing1621
Missing (%)22.0%
Memory size57.8 KiB
2025-02-04T05:02:16.222457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length155
Median length60
Mean length19.377083
Min length4

Characters and Unicode

Total characters111612
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1123 ?
Unique (%)19.5%

Sample

1st rowMSSN TORTLLA
2nd rowflipbelt level terrain waist pouch, neon yellow, large/32-35
3rd rowflipbelt level terrain waist pouch, neon yellow, large/32-35
4th rowTC NCTN GUM 2MG COOL
5th rowWNDRFL RSTD&SALTED
ValueCountFrequency (%)
hyv 429
 
2.1%
oz 284
 
1.4%
12 233
 
1.2%
s 231
 
1.1%
216
 
1.1%
ben 196
 
1.0%
cheese 170
 
0.8%
sausage 156
 
0.8%
lf 150
 
0.7%
milk 150
 
0.7%
Other values (2393) 17961
89.0%
2025-02-04T05:02:17.169273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14418
 
12.9%
E 8283
 
7.4%
S 6105
 
5.5%
A 5668
 
5.1%
R 5635
 
5.0%
L 5146
 
4.6%
C 4744
 
4.3%
O 4697
 
4.2%
N 4576
 
4.1%
I 4167
 
3.7%
Other values (69) 48173
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14418
 
12.9%
E 8283
 
7.4%
S 6105
 
5.5%
A 5668
 
5.1%
R 5635
 
5.0%
L 5146
 
4.6%
C 4744
 
4.3%
O 4697
 
4.2%
N 4576
 
4.1%
I 4167
 
3.7%
Other values (69) 48173
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14418
 
12.9%
E 8283
 
7.4%
S 6105
 
5.5%
A 5668
 
5.1%
R 5635
 
5.0%
L 5146
 
4.6%
C 4744
 
4.3%
O 4697
 
4.2%
N 4576
 
4.1%
I 4167
 
3.7%
Other values (69) 48173
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14418
 
12.9%
E 8283
 
7.4%
S 6105
 
5.5%
A 5668
 
5.1%
R 5635
 
5.0%
L 5146
 
4.6%
C 4744
 
4.3%
O 4697
 
4.2%
N 4576
 
4.1%
I 4167
 
3.7%
Other values (69) 48173
43.2%

itemNumber
Categorical

High correlation  Missing 

Distinct47
Distinct (%)30.7%
Missing7228
Missing (%)97.9%
Memory size57.8 KiB
4023
92 
013562300631
 
6
036000162905
 
5
078742112138
 
2
4011
 
2
Other values (42)
46 

Length

Max length12
Median length4
Mean length7.0326797
Min length4

Characters and Unicode

Total characters1076
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)24.8%

Sample

1st row4023
2nd row4023
3rd row4023
4th row4023
5th row000980000069

Common Values

ValueCountFrequency (%)
4023 92
 
1.2%
013562300631 6
 
0.1%
036000162905 5
 
0.1%
078742112138 2
 
< 0.1%
4011 2
 
< 0.1%
021000040247 2
 
< 0.1%
078742333960 2
 
< 0.1%
037466017631 2
 
< 0.1%
085718300512 2
 
< 0.1%
052100587967 1
 
< 0.1%
Other values (37) 37
 
0.5%
(Missing) 7228
97.9%

Length

2025-02-04T05:02:17.433637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4023 92
60.1%
013562300631 6
 
3.9%
036000162905 5
 
3.3%
078742333960 2
 
1.3%
037466017631 2
 
1.3%
085718300512 2
 
1.3%
021000040247 2
 
1.3%
4011 2
 
1.3%
078742112138 2
 
1.3%
011594404013 1
 
0.7%
Other values (37) 37
24.2%

Most occurring characters

ValueCountFrequency (%)
0 275
25.6%
2 162
15.1%
3 161
15.0%
4 151
14.0%
1 101
 
9.4%
6 57
 
5.3%
7 50
 
4.6%
8 47
 
4.4%
5 45
 
4.2%
9 27
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 275
25.6%
2 162
15.1%
3 161
15.0%
4 151
14.0%
1 101
 
9.4%
6 57
 
5.3%
7 50
 
4.6%
8 47
 
4.4%
5 45
 
4.2%
9 27
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 275
25.6%
2 162
15.1%
3 161
15.0%
4 151
14.0%
1 101
 
9.4%
6 57
 
5.3%
7 50
 
4.6%
8 47
 
4.4%
5 45
 
4.2%
9 27
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 275
25.6%
2 162
15.1%
3 161
15.0%
4 151
14.0%
1 101
 
9.4%
6 57
 
5.3%
7 50
 
4.6%
8 47
 
4.4%
5 45
 
4.2%
9 27
 
2.5%

originalMetaBriteQuantityPurchased
Categorical

High correlation  Missing 

Distinct2
Distinct (%)13.3%
Missing7366
Missing (%)99.8%
Memory size57.8 KiB
1.0
12 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12
 
0.2%
2.0 3
 
< 0.1%
(Missing) 7366
99.8%

Length

2025-02-04T05:02:17.756066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:17.958115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12
80.0%
2.0 3
 
20.0%

Most occurring characters

ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 12
26.7%
2 3
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 12
26.7%
2 3
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 12
26.7%
2 3
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
1 12
26.7%
2 3
 
6.7%

pointsEarned
Real number (ℝ)

High correlation  Missing 

Distinct277
Distinct (%)29.9%
Missing6454
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean140.50831
Minimum4.5
Maximum870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:18.231258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile5
Q128.05
median50
Q3165.45
95-th percentile870
Maximum870
Range865.5
Interquartile range (IQR)137.4

Descriptive statistics

Standard deviation223.00559
Coefficient of variation (CV)1.5871346
Kurtosis5.959663
Mean140.50831
Median Absolute Deviation (MAD)30.1
Skewness2.6550999
Sum130251.2
Variance49731.495
MonotonicityNot monotonic
2025-02-04T05:02:18.529099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 123
 
1.7%
210 89
 
1.2%
870 70
 
0.9%
5 70
 
0.9%
25 60
 
0.8%
30 40
 
0.5%
34.9 21
 
0.3%
55.9 20
 
0.3%
10 19
 
0.3%
19.9 13
 
0.2%
Other values (267) 402
 
5.4%
(Missing) 6454
87.4%
ValueCountFrequency (%)
4.5 1
 
< 0.1%
5 70
0.9%
10 19
 
0.3%
12.5 3
 
< 0.1%
12.9 1
 
< 0.1%
15 2
 
< 0.1%
16.2 1
 
< 0.1%
16.3 1
 
< 0.1%
16.5 1
 
< 0.1%
16.7 1
 
< 0.1%
ValueCountFrequency (%)
870 70
0.9%
844.2 2
 
< 0.1%
294 1
 
< 0.1%
292 1
 
< 0.1%
289 1
 
< 0.1%
287 1
 
< 0.1%
285.5 1
 
< 0.1%
280 3
 
< 0.1%
277.5 1
 
< 0.1%
274 1
 
< 0.1%

targetPrice
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing7003
Missing (%)94.9%
Memory size57.8 KiB
800
299 
77
79 

Length

Max length3
Median length3
Mean length2.7910053
Min length2

Characters and Unicode

Total characters1055
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row800
2nd row800
3rd row800
4th row800
5th row800

Common Values

ValueCountFrequency (%)
800 299
 
4.1%
77 79
 
1.1%
(Missing) 7003
94.9%

Length

2025-02-04T05:02:18.715925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:18.815269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
800 299
79.1%
77 79
 
20.9%

Most occurring characters

ValueCountFrequency (%)
0 598
56.7%
8 299
28.3%
7 158
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 598
56.7%
8 299
28.3%
7 158
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 598
56.7%
8 299
28.3%
7 158
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 598
56.7%
8 299
28.3%
7 158
 
15.0%

competitiveProduct
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing6736
Missing (%)91.3%
Memory size57.8 KiB
True
 
468
False
 
177
(Missing)
6736 
ValueCountFrequency (%)
True 468
 
6.3%
False 177
 
2.4%
(Missing) 6736
91.3%
2025-02-04T05:02:18.890898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

originalFinalPrice
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing7372
Missing (%)99.9%
Memory size57.8 KiB
1.00
10.00

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.00
2nd row1.00
3rd row10.00
4th row1.00
5th row1.00

Common Values

ValueCountFrequency (%)
1.00 6
 
0.1%
10.00 3
 
< 0.1%
(Missing) 7372
99.9%

Length

2025-02-04T05:02:19.020173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:19.129863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.00 6
66.7%
10.00 3
33.3%

Most occurring characters

ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

originalMetaBriteItemPrice
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing7372
Missing (%)99.9%
Memory size57.8 KiB
1.00
10.00

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.00
2nd row1.00
3rd row10.00
4th row1.00
5th row1.00

Common Values

ValueCountFrequency (%)
1.00 6
 
0.1%
10.00 3
 
< 0.1%
(Missing) 7372
99.9%

Length

2025-02-04T05:02:19.249247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-04T05:02:19.344005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.00 6
66.7%
10.00 3
33.3%

Most occurring characters

ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21
53.8%
1 9
23.1%
. 9
23.1%

deleted
Boolean

Constant  Missing 

Distinct1
Distinct (%)11.1%
Missing7372
Missing (%)99.9%
Memory size57.8 KiB
True
 
9
(Missing)
7372 
ValueCountFrequency (%)
True 9
 
0.1%
(Missing) 7372
99.9%
2025-02-04T05:02:19.404921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

priceAfterCoupon
Real number (ℝ)

High correlation  Missing 

Distinct334
Distinct (%)34.9%
Missing6425
Missing (%)87.0%
Infinite0
Infinite (%)0.0%
Mean10.393358
Minimum0.19
Maximum245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:19.535118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.56
Q12.685
median4.99
Q311.99
95-th percentile28.57
Maximum245
Range244.81
Interquartile range (IQR)9.305

Descriptive statistics

Standard deviation18.695615
Coefficient of variation (CV)1.7988041
Kurtosis75.037528
Mean10.393358
Median Absolute Deviation (MAD)3.2
Skewness7.4840527
Sum9936.05
Variance349.52601
MonotonicityNot monotonic
2025-02-04T05:02:19.730961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.57 50
 
0.7%
11.99 47
 
0.6%
22.97 44
 
0.6%
3.99 39
 
0.5%
1 25
 
0.3%
5.99 24
 
0.3%
0.56 23
 
0.3%
2.99 21
 
0.3%
4.99 20
 
0.3%
3.49 20
 
0.3%
Other values (324) 643
 
8.7%
(Missing) 6425
87.0%
ValueCountFrequency (%)
0.19 2
 
< 0.1%
0.5 20
0.3%
0.54 8
 
0.1%
0.56 23
0.3%
0.6 1
 
< 0.1%
0.77 2
 
< 0.1%
0.79 2
 
< 0.1%
0.8 1
 
< 0.1%
0.88 4
 
0.1%
0.89 1
 
< 0.1%
ValueCountFrequency (%)
245 1
< 0.1%
223.36 2
< 0.1%
180 1
< 0.1%
168.84 1
< 0.1%
115.96 1
< 0.1%
100 2
< 0.1%
95.84 2
< 0.1%
80 1
< 0.1%
79.84 1
< 0.1%
56 2
< 0.1%

metabriteCampaignId
Text

Missing 

Distinct75
Distinct (%)8.7%
Missing6518
Missing (%)88.3%
Memory size57.8 KiB
2025-02-04T05:02:20.119139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length46
Mean length28.724218
Min length11

Characters and Unicode

Total characters24789
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)3.5%

Sample

1st rowKRAFT SINGLES
2nd rowJUST BARE FRESH CHICKEN BREAST FILETS
3rd rowJUST BARE FRESH CHICKEN BREAST FILETS
4th rowDORITOS NACHO CHEESE MULTI SERVE
5th rowDORITOS NACHO CHEESE MULTI SERVE
ValueCountFrequency (%)
and 207
 
4.6%
200
 
4.4%
cream 186
 
4.1%
12 183
 
4.0%
ice 181
 
4.0%
ben 180
 
4.0%
jerrys 180
 
4.0%
pack 173
 
3.8%
cheese 123
 
2.7%
serve 113
 
2.5%
Other values (195) 2813
62.0%
2025-02-04T05:02:20.671820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3676
14.8%
E 3048
12.3%
R 1710
 
6.9%
S 1691
 
6.8%
A 1628
 
6.6%
C 1412
 
5.7%
I 1309
 
5.3%
N 1253
 
5.1%
T 942
 
3.8%
O 861
 
3.5%
Other values (34) 7259
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24789
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3676
14.8%
E 3048
12.3%
R 1710
 
6.9%
S 1691
 
6.8%
A 1628
 
6.6%
C 1412
 
5.7%
I 1309
 
5.3%
N 1253
 
5.1%
T 942
 
3.8%
O 861
 
3.5%
Other values (34) 7259
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24789
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3676
14.8%
E 3048
12.3%
R 1710
 
6.9%
S 1691
 
6.8%
A 1628
 
6.6%
C 1412
 
5.7%
I 1309
 
5.3%
N 1253
 
5.1%
T 942
 
3.8%
O 861
 
3.5%
Other values (34) 7259
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24789
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3676
14.8%
E 3048
12.3%
R 1710
 
6.9%
S 1691
 
6.8%
A 1628
 
6.6%
C 1412
 
5.7%
I 1309
 
5.3%
N 1253
 
5.1%
T 942
 
3.8%
O 861
 
3.5%
Other values (34) 7259
29.3%

receipt_item_id
Text

Unique 

Distinct7381
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:20.908016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length27
Mean length26.988619
Min length26

Characters and Unicode

Total characters199203
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7381 ?
Unique (%)100.0%

Sample

1st row5ff1e1cd0a720f052300056f_1
2nd row5ff5d1f20a720f05230005db_1
3rd row5ff5d1f20a720f05230005db_2
4th row5ff5d1f20a720f05230005db_3
5th row5ff5d1f20a720f05230005db_4
ValueCountFrequency (%)
5ff1e1cd0a720f052300056f_1 1
 
< 0.1%
5ffe22a20a720f05ac0061d7_1 1
 
< 0.1%
5ff5d1f20a720f05230005db_2 1
 
< 0.1%
5ff5d1f20a720f05230005db_3 1
 
< 0.1%
5ff5d1f20a720f05230005db_4 1
 
< 0.1%
5ff74fd10a720f0523000619_1 1
 
< 0.1%
5ff7942e0a720f0523000637_1 1
 
< 0.1%
5ff7942e0a720f0523000637_2 1
 
< 0.1%
5ff8da390a7214adca000013_1 1
 
< 0.1%
5ff8da620a7214adca00001d_1 1
 
< 0.1%
Other values (7371) 7371
99.9%
2025-02-04T05:02:21.354098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 62920
31.6%
2 16136
 
8.1%
a 15529
 
7.8%
f 11783
 
5.9%
7 11185
 
5.6%
1 10606
 
5.3%
5 10473
 
5.3%
3 9720
 
4.9%
6 9486
 
4.8%
_ 7381
 
3.7%
Other values (7) 33984
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62920
31.6%
2 16136
 
8.1%
a 15529
 
7.8%
f 11783
 
5.9%
7 11185
 
5.6%
1 10606
 
5.3%
5 10473
 
5.3%
3 9720
 
4.9%
6 9486
 
4.8%
_ 7381
 
3.7%
Other values (7) 33984
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62920
31.6%
2 16136
 
8.1%
a 15529
 
7.8%
f 11783
 
5.9%
7 11185
 
5.6%
1 10606
 
5.3%
5 10473
 
5.3%
3 9720
 
4.9%
6 9486
 
4.8%
_ 7381
 
3.7%
Other values (7) 33984
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62920
31.6%
2 16136
 
8.1%
a 15529
 
7.8%
f 11783
 
5.9%
7 11185
 
5.6%
1 10606
 
5.3%
5 10473
 
5.3%
3 9720
 
4.9%
6 9486
 
4.8%
_ 7381
 
3.7%
Other values (7) 33984
17.1%

items_in_receipt_list
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)0.7%
Missing464
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean240.01084
Minimum1
Maximum689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:21.502556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q193
median167
Q3309
95-th percentile689
Maximum689
Range688
Interquartile range (IQR)216

Descriptive statistics

Standard deviation221.92227
Coefficient of variation (CV)0.92463433
Kurtosis-0.28839641
Mean240.01084
Median Absolute Deviation (MAD)136
Skewness0.96207664
Sum1660155
Variance49249.492
MonotonicityNot monotonic
2025-02-04T05:02:21.709701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
689 459
 
6.2%
1 458
 
6.2%
670 450
 
6.1%
599 381
 
5.2%
5 372
 
5.0%
133 248
 
3.4%
303 217
 
2.9%
214 203
 
2.8%
212 194
 
2.6%
335 185
 
2.5%
Other values (39) 3750
50.8%
(Missing) 464
 
6.3%
ValueCountFrequency (%)
1 458
6.2%
2 149
 
2.0%
3 32
 
0.4%
4 174
 
2.4%
5 372
5.0%
6 23
 
0.3%
7 15
 
0.2%
8 2
 
< 0.1%
9 90
 
1.2%
10 115
 
1.6%
ValueCountFrequency (%)
689 459
6.2%
670 450
6.1%
599 381
5.2%
348 127
 
1.7%
341 124
 
1.7%
335 185
2.5%
309 183
 
2.5%
303 217
2.9%
240 148
 
2.0%
229 99
 
1.3%

summary_list_difference_count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7
Distinct (%)0.1%
Missing513
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean-0.064647641
Minimum-10
Maximum4
Zeros6756
Zeros (%)91.5%
Negative92
Negative (%)1.2%
Memory size57.8 KiB
2025-02-04T05:02:21.858952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81178308
Coefficient of variation (CV)-12.557041
Kurtosis141.21441
Mean-0.064647641
Median Absolute Deviation (MAD)0
Skewness-11.677228
Sum-444
Variance0.65899176
MonotonicityNot monotonic
2025-02-04T05:02:21.989747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6756
91.5%
-1 48
 
0.7%
-10 44
 
0.6%
2 16
 
0.2%
4 2
 
< 0.1%
1 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 513
 
7.0%
ValueCountFrequency (%)
-10 44
 
0.6%
-1 48
 
0.7%
0 6756
91.5%
1 1
 
< 0.1%
2 16
 
0.2%
3 1
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
4 2
 
< 0.1%
3 1
 
< 0.1%
2 16
 
0.2%
1 1
 
< 0.1%
0 6756
91.5%
-1 48
 
0.7%
-10 44
 
0.6%

hours_diff
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct67
Distinct (%)1.1%
Missing1301
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean7.4424764
Minimum0
Maximum1891.8567
Zeros439
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:22.196774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.056666667
median0.12944444
Q30.18805556
95-th percentile5.5
Maximum1891.8567
Range1891.8567
Interquartile range (IQR)0.13138889

Descriptive statistics

Standard deviation102.9143
Coefficient of variation (CV)13.827965
Kurtosis240.0396
Mean7.4424764
Median Absolute Deviation (MAD)0.071944444
Skewness15.443841
Sum45250.256
Variance10591.354
MonotonicityNot monotonic
2025-02-04T05:02:22.414113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0002777777778 593
 
8.0%
1.181944444 459
 
6.2%
5.5 450
 
6.1%
0 439
 
5.9%
0.1447222222 203
 
2.8%
0.2241666667 194
 
2.6%
0.1030555556 186
 
2.5%
0.1191666667 183
 
2.5%
0.1880555556 176
 
2.4%
0.1194444444 155
 
2.1%
Other values (57) 3042
41.2%
(Missing) 1301
17.6%
ValueCountFrequency (%)
0 439
5.9%
0.0002777777778 593
8.0%
0.0005555555556 62
 
0.8%
0.0008333333333 14
 
0.2%
0.001111111111 8
 
0.1%
0.001388888889 2
 
< 0.1%
0.001666666667 7
 
0.1%
0.001944444444 10
 
0.1%
0.002222222222 9
 
0.1%
0.0025 13
 
0.2%
ValueCountFrequency (%)
1891.856667 5
0.1%
1555.365 5
0.1%
1537.339444 5
0.1%
1440.754722 11
0.1%
102.0030556 4
 
0.1%
72.58861111 1
 
< 0.1%
48.00277778 2
 
< 0.1%
48.00166667 1
 
< 0.1%
48.00111111 2
 
< 0.1%
48.00083333 1
 
< 0.1%

minutes_diff
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct67
Distinct (%)1.1%
Missing1301
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean446.54858
Minimum0
Maximum113511.4
Zeros439
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-02-04T05:02:22.626280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.4
median7.7666667
Q311.283333
95-th percentile330
Maximum113511.4
Range113511.4
Interquartile range (IQR)7.8833333

Descriptive statistics

Standard deviation6174.8581
Coefficient of variation (CV)13.827965
Kurtosis240.0396
Mean446.54858
Median Absolute Deviation (MAD)4.3166667
Skewness15.443841
Sum2715015.4
Variance38128873
MonotonicityNot monotonic
2025-02-04T05:02:22.838421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01666666667 593
 
8.0%
70.91666667 459
 
6.2%
330 450
 
6.1%
0 439
 
5.9%
8.683333333 203
 
2.8%
13.45 194
 
2.6%
6.183333333 186
 
2.5%
7.15 183
 
2.5%
11.28333333 176
 
2.4%
7.166666667 155
 
2.1%
Other values (57) 3042
41.2%
(Missing) 1301
17.6%
ValueCountFrequency (%)
0 439
5.9%
0.01666666667 593
8.0%
0.03333333333 62
 
0.8%
0.05 14
 
0.2%
0.06666666667 8
 
0.1%
0.08333333333 2
 
< 0.1%
0.1 7
 
0.1%
0.1166666667 10
 
0.1%
0.1333333333 9
 
0.1%
0.15 13
 
0.2%
ValueCountFrequency (%)
113511.4 5
0.1%
93321.9 5
0.1%
92240.36667 5
0.1%
86445.28333 11
0.1%
6120.183333 4
 
0.1%
4355.316667 1
 
< 0.1%
2880.166667 2
 
< 0.1%
2880.1 1
 
< 0.1%
2880.066667 2
 
< 0.1%
2880.05 1
 
< 0.1%

Interactions

2025-02-04T05:01:46.725573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:02.824985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:15.575340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:28.147706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:41.297575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:22.458767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:34.554729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:30.239581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:42.329134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:22.413473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:44.610797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:06.339822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:18.495517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:34.983063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:46.952091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:03.016260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:15.753417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:28.317157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:43.330892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:22.614714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:37.555079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:30.403247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:44.379447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:23.427017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:45.848175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:06.602568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:18.686956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:35.256102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:47.137682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:03.174774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:15.958705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:28.486543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:47.317019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:22.770164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:42.843794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:30.590534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:47.027621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:24.456153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:47.070121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:06.883231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:18.863557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:35.548753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:47.353791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:03.333764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:16.127676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:28.647708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:49.376251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:22.962396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:46.461463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:30.766128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:51.846868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:25.529558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:48.284701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:07.174949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:19.059290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:35.774542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:50.316815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:06.199085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:18.641603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:32.103833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:53.066426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:25.459515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:51.471611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:33.394131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:55.551938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:28.123313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:50.360344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:10.013260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:21.618660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:38.220512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:50.632216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:06.500972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:18.904629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:32.374171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:55.138696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:25.743312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:55.382355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:33.671402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:57.626457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:32.427552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:51.582931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:10.189040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:21.931860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:38.391941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:53.559530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:09.675426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:22.898949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:35.855444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:02.137159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:29.303213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:00.917737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:37.146521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:03.477154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:34.315897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:55.467292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:13.172410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:25.402350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:41.519168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:53.725120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:09.882012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:23.086489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:36.029726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:04.205115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:29.480391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:04.511106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:37.325125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:05.498322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:35.324449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:56.907826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:13.356708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:25.590414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:41.724242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:55.756702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:12.467144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:25.098299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:38.278279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:08.042280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:31.493703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:10.303570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:39.340268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:09.279291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:37.086572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:58.930975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:15.463129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:27.659623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:43.716858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:56.827668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:13.562569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:26.144533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:39.365451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:09.876999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:32.559634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:14.340122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:40.339204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:11.037163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:38.789864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:59.360628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:16.518963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:32.418525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:44.794716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:58.032366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:14.831140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:27.427801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:40.629068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:12.433093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:33.837553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:17.331167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:41.599165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:13.532115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:39.239106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:01.427191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:17.769733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:33.847003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:46.026357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:58.227649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:15.018824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:27.603006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:40.794255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:14.948458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:34.001263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:21.460911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:41.774656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:16.263708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:40.669508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:02.645331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:17.945326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:34.161873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:46.202236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:58.408004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:15.196116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:27.775544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:40.962476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:18.352987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:34.184587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:24.479312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:41.957428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:18.336536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:42.226301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:03.848305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:18.132746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:34.392463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:46.373874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:58.584781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:15.380424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:27.968117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:57:41.132106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:20.421905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:58:34.355173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:27.380198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T04:59:42.146580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:20.358355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:00:43.496823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:05.083207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:18.321080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:34.697049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-04T05:01:46.542044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-04T05:02:23.050395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
competitiveProductcompetitorRewardsGroupdiscountedItemPricefinalPricehours_diffitemNumberitemPriceitems_in_receipt_listminutes_diffneedsFetchReviewneedsFetchReviewReasonoriginalFinalPriceoriginalMetaBriteBarcodeoriginalMetaBriteDescriptionoriginalMetaBriteItemPriceoriginalMetaBriteQuantityPurchasedpartnerItemIdpointsEarnedpointsPayerIdpriceAfterCouponpurchasedItemCountquantityPurchasedrewardsProductPartnerIdsummary_list_difference_counttargetPricetotal_receipt_points_earnedtotal_receipt_spentuserFlaggedBarcodeuserFlaggedDescriptionuserFlaggedPriceuserFlaggedQuantity
competitiveProduct1.0001.0000.7680.8130.0001.0000.0810.4320.0000.0000.5931.0001.0000.0001.0001.0000.5610.7340.2370.6420.4320.2350.6470.0001.0000.3820.4271.0000.0001.0001.000
competitorRewardsGroup1.0001.0000.7090.7091.000NaN0.0000.2401.0001.0000.0000.0000.0000.0000.0000.0000.3890.0000.0000.6210.2340.1980.8691.0000.0000.0450.2420.0000.0000.0000.000
discountedItemPrice0.7680.7091.0001.000-0.0440.9241.000-0.018-0.0440.8121.0000.0000.4420.0000.0001.000-0.0350.9670.3421.0000.0050.4310.454NaN0.0000.037-0.0050.0000.0000.0000.000
finalPrice0.8130.7091.0001.000-0.1050.9241.000-0.081-0.1050.4790.8750.7070.6410.7620.7070.961-0.1030.9390.5411.000-0.0640.3980.5280.0490.9880.005-0.0490.8300.8660.9430.432
hours_diff0.0001.000-0.044-0.1051.0001.000-0.1040.7521.0000.0861.0000.0000.6640.9350.0000.1600.6240.1240.445-0.0090.7520.0390.3840.1150.9970.6960.7660.1601.0000.2320.011
itemNumber1.000NaN0.9240.9241.0001.0000.8381.0001.0001.0001.0000.0000.3610.0000.0001.0000.9230.9180.9670.0001.0000.8380.9111.0000.0000.8381.0000.0000.0000.0000.000
itemPrice0.0810.0001.0001.000-0.1040.8381.000-0.080-0.1040.2420.9891.0001.0001.0001.0000.000-0.1030.9390.2071.000-0.0640.3980.0000.0491.0000.005-0.0491.0001.0001.0001.000
items_in_receipt_list0.4320.240-0.018-0.0810.7521.000-0.0801.0000.7520.6710.3581.0001.0001.0001.0001.0000.6960.1150.331-0.3130.9990.2020.2610.1411.0000.7860.9381.0001.0001.0001.000
minutes_diff0.0001.000-0.044-0.1051.0001.000-0.1040.7521.0000.0861.0000.0000.6640.9350.0000.1600.6240.1240.445-0.0090.7520.0390.3840.1150.9970.6960.7660.1601.0000.2320.011
needsFetchReview0.0001.0000.8120.4790.0861.0000.2420.6710.0861.0001.0001.0001.0001.0001.0000.0000.3800.4820.328NaN0.6790.2190.3720.5541.0000.7010.6790.7131.0000.5530.481
needsFetchReviewReason0.5930.0001.0000.8751.0001.0000.9890.3581.0001.0001.0000.0000.0000.0000.0001.0000.5960.6710.956NaN0.3600.8540.9470.0720.0000.4680.3601.0001.0001.0001.000
originalFinalPrice1.0000.0000.0000.7070.0000.0001.0001.0000.0001.0000.0001.0000.7071.0000.7070.7070.9261.0001.0000.0001.0001.0000.7071.0001.0001.0001.0000.7070.0000.7070.707
originalMetaBriteBarcode1.0000.0000.4420.6410.6640.3611.0001.0000.6641.0000.0000.7071.0000.7620.7070.7070.2770.8530.7420.0001.0001.0000.6941.0000.8560.3111.0001.0000.0000.7070.707
originalMetaBriteDescription0.0000.0000.0000.7620.9350.0001.0001.0000.9351.0000.0001.0000.7621.0001.0001.0000.9351.0000.7620.0001.0001.0000.7621.0001.0000.4151.0000.7620.0001.0001.000
originalMetaBriteItemPrice1.0000.0000.0000.7070.0000.0001.0001.0000.0001.0000.0000.7070.7071.0001.0000.7070.9261.0001.0000.0001.0001.0000.7071.0001.0001.0001.0000.7070.0000.7070.707
originalMetaBriteQuantityPurchased1.0000.0001.0000.9610.1601.0000.0001.0000.1600.0001.0000.7070.7071.0000.7071.0000.9201.0001.0000.0001.0000.0000.9611.0001.0001.0001.0000.7070.0000.7070.707
partnerItemId0.5610.389-0.035-0.1030.6240.923-0.1030.6960.6240.3800.5960.9260.2770.9350.9260.9201.0000.0520.496-0.1710.6930.1500.3780.1070.4080.5850.6860.5440.5610.3450.371
pointsEarned0.7340.0000.9670.9390.1240.9180.9390.1150.1240.4820.6711.0000.8531.0001.0001.0000.0521.0000.6200.9580.1150.3310.620NaN0.9920.4990.3280.9871.0000.9221.000
pointsPayerId0.2370.0000.3420.5410.4450.9670.2070.3310.4450.3280.9561.0000.7420.7621.0001.0000.4960.6201.0000.3680.3310.2261.0001.0000.9960.3810.4270.9910.8620.9871.000
priceAfterCoupon0.6420.6211.0001.000-0.0090.0001.000-0.313-0.009NaNNaN0.0000.0000.0000.0000.000-0.1710.9580.3681.000-0.2070.3740.409NaN0.000-0.223-0.3120.0000.0000.0000.000
purchasedItemCount0.4320.2340.005-0.0640.7521.000-0.0640.9990.7520.6790.3601.0001.0001.0001.0001.0000.6930.1150.331-0.2071.0000.1980.2610.1011.0000.7880.9371.0001.0001.0001.000
quantityPurchased0.2350.1980.4310.3980.0390.8380.3980.2020.0390.2190.8541.0001.0001.0001.0000.0000.1500.3310.2260.3740.1981.0000.2180.0650.0470.2310.1330.3180.5200.3570.701
rewardsProductPartnerId0.6470.8690.4540.5280.3840.9110.0000.2610.3840.3720.9470.7070.6940.7620.7070.9610.3780.6201.0000.4090.2610.2181.0000.0000.9960.3520.3310.9920.8620.6920.990
summary_list_difference_count0.0001.000NaN0.0490.1151.0000.0490.1410.1150.5540.0721.0001.0001.0001.0001.0000.107NaN1.000NaN0.1010.0650.0001.0001.0000.0750.1370.5250.6350.6300.370
targetPrice1.0000.0000.0000.9880.9970.0001.0001.0000.9971.0000.0001.0000.8561.0001.0001.0000.4080.9920.9960.0001.0000.0470.9961.0001.0000.9241.0000.9850.0000.9781.000
total_receipt_points_earned0.3820.0450.0370.0050.6960.8380.0050.7860.6960.7010.4681.0000.3110.4151.0001.0000.5850.4990.381-0.2230.7880.2310.3520.0750.9241.0000.7400.1050.3910.1950.150
total_receipt_spent0.4270.242-0.005-0.0490.7661.000-0.0490.9380.7660.6790.3601.0001.0001.0001.0001.0000.6860.3280.427-0.3120.9370.1330.3310.1371.0000.7401.0001.0001.0001.0001.000
userFlaggedBarcode1.0000.0000.0000.8300.1600.0001.0001.0000.1600.7131.0000.7071.0000.7620.7070.7070.5440.9870.9910.0001.0000.3180.9920.5250.9850.1051.0001.0000.9980.8020.520
userFlaggedDescription0.0000.0000.0000.8661.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.5611.0000.8620.0001.0000.5200.8620.6350.0000.3911.0000.9981.0000.8820.529
userFlaggedPrice1.0000.0000.0000.9430.2320.0001.0001.0000.2320.5531.0000.7070.7071.0000.7070.7070.3450.9220.9870.0001.0000.3570.6920.6300.9780.1951.0000.8020.8821.0000.479
userFlaggedQuantity1.0000.0000.0000.4320.0110.0001.0001.0000.0110.4811.0000.7070.7071.0000.7070.7070.3711.0001.0000.0001.0000.7010.9900.3701.0000.1501.0000.5200.5290.4791.000

Missing values

2025-02-04T05:01:58.994684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-04T05:01:59.539443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-04T05:02:00.473844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

receipt_idpurchasedItemCountcreatedatedatescannedfinisheddatemodifydatepointsawardeddatepurchasedatetotal_receipt_points_earnedtotal_receipt_spentbarcodedescriptionfinalPriceitemPriceneedsFetchReviewpartnerItemIdpreventTargetGapPointsquantityPurchaseduserFlaggedBarcodeuserFlaggedNewItemuserFlaggedPriceuserFlaggedQuantityneedsFetchReviewReasonpointsNotAwardedReasonpointsPayerIdrewardsGrouprewardsProductPartnerIduserFlaggedDescriptionoriginalMetaBriteBarcodeoriginalMetaBriteDescriptionbrandCodecompetitorRewardsGroupdiscountedItemPriceoriginalReceiptItemTextitemNumberoriginalMetaBriteQuantityPurchasedpointsEarnedtargetPricecompetitiveProductoriginalFinalPriceoriginalMetaBriteItemPricedeletedpriceAfterCouponmetabriteCampaignIdreceipt_item_iditems_in_receipt_listsummary_list_difference_counthours_diffminutes_diff
05ff1e1cd0a720f052300056f1.02021-01-03 15:25:012021-01-03 15:25:012021-01-03 15:25:022021-01-03 15:25:022021-01-03 15:25:022021-01-03 15:25:015.02.230000NoneMSSN TORTLLA2.232.23NaN1009NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneMISSIONTACO BELL TACO SHELLS2.23MSSN TORTLLANoneNaNNoneNoneNaNNoneNoneNaNNoneNone5ff1e1cd0a720f052300056f_11.00.00.0002780.016667
15ff5d1f20a720f05230005db4.02021-01-06 15:06:262021-01-06 15:06:262021-01-06 15:06:272021-01-06 15:06:272021-01-06 15:06:272021-01-05 15:06:265.018.200001021000051113None4.554.55NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneSARGENTO GRATED PARMESAN CHEESE5e7cf838f221c312e698a628NoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneTrueNoneNoneNaNNoneNone5ff5d1f20a720f05230005db_14.00.00.0002780.016667
25ff5d1f20a720f05230005db4.02021-01-06 15:06:262021-01-06 15:06:262021-01-06 15:06:272021-01-06 15:06:272021-01-06 15:06:272021-01-05 15:06:265.018.200001021000051113None4.554.55NaN2NaN1.0NoneNaNNoneNaNNoneNoneNoneSARGENTO GRATED PARMESAN CHEESE5e7cf838f221c312e698a628NoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneTrueNoneNoneNaNNoneNone5ff5d1f20a720f05230005db_24.00.00.0002780.016667
35ff5d1f20a720f05230005db4.02021-01-06 15:06:262021-01-06 15:06:262021-01-06 15:06:272021-01-06 15:06:272021-01-06 15:06:272021-01-05 15:06:265.018.200001021000051113None4.554.55NaN3NaN1.0NoneNaNNoneNaNNoneNoneNoneSARGENTO GRATED PARMESAN CHEESE5e7cf838f221c312e698a628NoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneTrueNoneNoneNaNNoneNone5ff5d1f20a720f05230005db_34.00.00.0002780.016667
45ff5d1f20a720f05230005db4.02021-01-06 15:06:262021-01-06 15:06:262021-01-06 15:06:272021-01-06 15:06:272021-01-06 15:06:272021-01-05 15:06:265.018.200001021000051113None4.554.55NaN4NaN1.0NoneNaNNoneNaNNoneNoneNoneSARGENTO GRATED PARMESAN CHEESE5e7cf838f221c312e698a628NoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneTrueNoneNoneNaNNoneNone5ff5d1f20a720f05230005db_44.00.00.0002780.016667
55ff74fd10a720f05230006191.02021-01-07 18:15:452021-01-07 18:15:452021-01-07 18:15:472021-01-07 18:15:472021-01-07 18:15:472021-01-06 18:15:455.01.000000043000204399Jell-O Instant Pudding & Pie Filling French Vanilla, 3.4 Oz11.00NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneNone559c2234e4b06aca36af13c6NoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneNaNNoneNoneNaNNoneNone5ff74fd10a720f0523000619_11.00.00.0005560.033333
65ff7942e0a720f05230006372.02021-01-05 23:07:262021-01-05 23:07:262021-01-07 23:07:362021-01-07 23:07:362021-01-07 05:07:292021-01-04 23:07:262005.01.0000001234NoneNoneNaNTrue2TrueNaN1234TrueNoneNaNUSER_FLAGGEDNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneNaNNoneNoneNaNNoneNone5ff7942e0a720f0523000637_11.0-1.030.0008331800.050000
75ff7942e0a720f05230006372.02021-01-05 23:07:262021-01-05 23:07:262021-01-07 23:07:362021-01-07 23:07:362021-01-07 05:07:292021-01-04 23:07:262005.01.0000004011ITEM NOT FOUND11.00NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneNaNNoneNoneNaNNoneNone5ff7942e0a720f0523000637_21.0-1.030.0008331800.050000
85ff8da390a7214adca0000131.02021-01-08 22:18:332021-01-08 22:18:332021-01-08 22:18:342021-01-08 22:18:342021-01-08 22:18:342021-01-01 00:00:00350.010.000000305210154278VASELINE COCOA BUTTER SKIN MOISTURIZER LOTION CONDITIONING RP 34.5 OZ10.0010.00NaN0NaN1.0NoneNaNNoneNaNNoneNone5332f5f6e4b03c9a25efd0b4VASELINE HAND AND BODY LOTION5332f5f6e4b03c9a25efd0b4NoneNoneNoneBRANDNoneNoneNoneNoneNaN100.0NoneNaNNoneNoneNaNNoneNone5ff8da390a7214adca000013_11.00.00.0002780.016667
95ff8da620a7214adca00001d1.02021-01-08 22:19:142021-01-08 22:19:14NaT2021-01-08 22:19:152021-01-08 22:19:142021-01-07 22:19:14750.01.000000075925306254None11.00NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneSARGENTO NATURAL SHREDDED CHEESE 6OZ OR LARGER5e7cf838f221c312e698a628NoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneTrueNoneNoneNaNNoneNone5ff8da620a7214adca00001d_11.00.00.0000000.000000
receipt_idpurchasedItemCountcreatedatedatescannedfinisheddatemodifydatepointsawardeddatepurchasedatetotal_receipt_points_earnedtotal_receipt_spentbarcodedescriptionfinalPriceitemPriceneedsFetchReviewpartnerItemIdpreventTargetGapPointsquantityPurchaseduserFlaggedBarcodeuserFlaggedNewItemuserFlaggedPriceuserFlaggedQuantityneedsFetchReviewReasonpointsNotAwardedReasonpointsPayerIdrewardsGrouprewardsProductPartnerIduserFlaggedDescriptionoriginalMetaBriteBarcodeoriginalMetaBriteDescriptionbrandCodecompetitorRewardsGroupdiscountedItemPriceoriginalReceiptItemTextitemNumberoriginalMetaBriteQuantityPurchasedpointsEarnedtargetPricecompetitiveProductoriginalFinalPriceoriginalMetaBriteItemPricedeletedpriceAfterCouponmetabriteCampaignIdreceipt_item_iditems_in_receipt_listsummary_list_difference_counthours_diffminutes_diff
7371603aab2f0a7217c72c00023a2.02021-02-27 20:27:27.0002021-02-27 20:27:27.000NaT2021-02-27 20:27:28.000NaT2020-08-1725.034.959999B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone22.97mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, whiteNoneNaNNoneNoneNaNNoneNoneNaN22.97None603aab2f0a7217c72c00023a_12.00.0NaNNaN
7372603aab2f0a7217c72c00023a2.02021-02-27 20:27:27.0002021-02-27 20:27:27.000NaT2021-02-27 20:27:28.000NaT2020-08-1725.034.959999B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone11.99thindust summer face mask - sun protection neck gaiter for outdooractivitiesNoneNaNNoneNoneNaNNoneNoneNaN11.99None603aab2f0a7217c72c00023a_22.00.0NaNNaN
7373603b77170a7217c72c0002e62.02021-02-28 10:57:27.0002021-02-28 10:57:27.000NaT2021-02-28 10:57:28.000NaT2020-08-1725.034.959999B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone22.97mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, whiteNoneNaNNoneNoneNaNNoneNoneNaN22.97None603b77170a7217c72c0002e6_12.00.0NaNNaN
7374603b77170a7217c72c0002e62.02021-02-28 10:57:27.0002021-02-28 10:57:27.000NaT2021-02-28 10:57:28.000NaT2020-08-1725.034.959999B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone11.99thindust summer face mask - sun protection neck gaiter for outdooractivitiesNoneNaNNoneNoneNaNNoneNoneNaN11.99None603b77170a7217c72c0002e6_22.00.0NaNNaN
7375603bdbe10a7217c72c00033e2.02021-02-28 18:07:29.0002021-02-28 18:07:29.000NaT2021-02-28 18:07:30.000NaT2020-08-1725.034.959999B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone22.97mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, whiteNoneNaNNoneNoneNaNNoneNoneNaN22.97None603bdbe10a7217c72c00033e_12.00.0NaNNaN
7376603bdbe10a7217c72c00033e2.02021-02-28 18:07:29.0002021-02-28 18:07:29.000NaT2021-02-28 18:07:30.000NaT2020-08-1725.034.959999B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone11.99thindust summer face mask - sun protection neck gaiter for outdooractivitiesNoneNaNNoneNoneNaNNoneNoneNaN11.99None603bdbe10a7217c72c00033e_22.00.0NaNNaN
7377603c4fea0a7217c72c000389NaN2021-03-01 02:22:34.9622021-03-01 02:22:34.962NaT2021-03-01 02:22:34.962NaTNaTNaNNaNNoneNoneNoneNaNNaNNoneNaNNaNNoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneNaNNoneNoneNaNNoneNone603c4fea0a7217c72c000389_1NaNNaNNaNNaN
7378603ca44a0a720fde100003d0NaN2021-03-01 08:22:34.2442021-03-01 08:22:34.244NaT2021-03-01 08:22:34.244NaTNaTNaNNaNNoneNoneNoneNaNNaNNoneNaNNaNNoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNaNNoneNoneNaNNoneNoneNaNNoneNone603ca44a0a720fde100003d0_1NaNNaNNaNNaN
7379603d59e70a7217c72c00045f2.02021-03-01 21:17:27.0002021-03-01 21:17:27.000NaT2021-03-01 21:17:28.000NaT2020-08-1725.034.959999B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone22.97mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, whiteNoneNaNNoneNoneNaNNoneNoneNaN22.97None603d59e70a7217c72c00045f_12.00.0NaNNaN
7380603d59e70a7217c72c00045f2.02021-03-01 21:17:27.0002021-03-01 21:17:27.000NaT2021-03-01 21:17:28.000NaT2020-08-1725.034.959999B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NoneNaNNoneNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNone11.99thindust summer face mask - sun protection neck gaiter for outdooractivitiesNoneNaNNoneNoneNaNNoneNoneNaN11.99None603d59e70a7217c72c00045f_22.00.0NaNNaN